I have three data sets of different lengths and I would like to plot density functions of all three on the same plot. This is straight forward with base graphics:
n <- c(rnorm(10000), rnorm(10000))
a <- c(rnorm(10001), rnorm(10001, 0, 2))
p <- c(rnorm(10002), rnorm(10002, 2, .5))
plot(density(n))
lines(density(a))
lines(density(p))
Which gives me something like this:
alt text http://www.cerebralmastication.com/wp-content/uploads/2009/10/density.png
But I really want to do this with GGPLOT2 because I want to add other features that are only available with GGPLOT2. It seems that GGPLOT really wants to take my empirical data and calculate the density for me. And it gives me a bunch of lip because my data sets are of different lengths. So how do I get these three densities to plot in GGPLOT2?
The secret to happiness in ggplot2 is to put everything in the "long" (or what I guess matrix oriented people would call "sparse") format:
df <- rbind(data.frame(x="n",value=n),
data.frame(x="a",value=a),
data.frame(x="p",value=p))
qplot(value, colour=x, data=df, geom="density")
If you don't want colors:
qplot(value, group=x, data=df, geom="density")
Related
I found some similar questions but the answers didn't solve my problem.
I try to plot a time series of to variables as a scatterplot and using the date to color the points. In this example, I created a simple dataset (see below) and I want to plot all data with timesteps in the 1960ties, 70ties, 80ties and 90ties with one colour respectively.
Using the standard plot command (plot(x,y,...)) it works the way it should, as I try using the ggplot library some strange happens, I guess I miss something. Has anyone an idea how to solve this and generate a correct plot?
Here is my code using the standard plot command with a colorbar
# generate data frame with test data
x <- seq(1,40)
y <- seq(1,40)
year <- c(rep(seq(1960,1969),2),seq(1970,1989,2),seq(1990,1999))
df <- data.frame(x,y,year)
# define interval and assing color to interval
myinterval <- seq(1959,1999,10)
mycolors <- rainbow(4)
colbreaks <- findInterval(df$year, vec = myinterval, left.open = T)
# basic plot
layout(array(1:2,c(1,2)),widths =c(5,1)) # divide the device area in two panels
par(oma=c(0,0,0,0), mar=c(3,3,3,3))
plot(x,y,pch=20,col = mycolors[colbreaks])
# add colorbar
ncols <- length(myinterval)-1
colbarlabs <- seq(1960,2000,10)
par(mar=c(5,0,5,5))
image(t(array(1:ncols, c(ncols,1))), col=mycolors, axes=F)
box()
axis(4, at=seq(0.5/(ncols-1)-1/(ncols-1),1+1/(ncols-1),1/(ncols-1)), labels=colbarlabs, cex.axis=1, las=1)
abline(h=seq(0.5/(ncols-1),1,1/(ncols-1)))
mtext("year",side=3,line=0.5,cex=1)
As I would like to use ggplot package, as I do for other plots, I tried this version with ggplot
# plot with ggplot
require(ggplot2)
ggplot(df, aes(x=x,y=y,color=year)) + geom_point() +
scale_colour_gradientn(colours= mycolors[colbreaks])
but it didn't work the way I thought it would. Obviously, there is something wrong with the color coding. Also, the colorbar looks strange. I also tried it with scale_color_manual and scale_color_gradient2 but I got more errors (Error in continuous_scale).
Any idea how to solve this and generate a plot according to the standard plot 3 including a colorbar.
I have a data frame data_2 and wish to create a Bland-Altman plot to compare the differences between the data in the columns alog1 vs. dig1.
Please help with the function for this and how to execute this. Would the function be barplot()?
Thanks for your time.
Another name for a Bland-Altman plot is a Tukey mean-difference plot. (I have nothing against Bland and Altman, but I think 'mean-difference' is more descriptive.) Note that this different from a boxplot (observe the pictures on the two Wikipedia pages). The mean-difference plot is simply a regular scatterplot, except that instead of plotting x versus y, you are plotting the difference x-y against the mean of x and y (or in your case, alog1 and dig1). Probably the easiest way to make this is to form these two new variables first, and then simply plot them as you would any other scatterplot. Here is some sample code:
mn <- (data_2$alog1 + data_2$dig1)/2
dif <- data_2$alog1 - data_2$dig1
plot(mn, dif)
If you wanted to add arguments to customize your plot, you could do that just as you normally would, for example:
plot(mn, dif, main="Bland-Altman plot", xlab="mean of alog1 & dig1",
ylab="difference between alog1 & dig1")
I have been trying to plot simple density plots using R as:
plot(density(Data$X1),col="red")
plot(density(Data$X2),col="green")
Since I want to compare, I'd like to plot both in one figure. But 'matplot' doesn't work!! I also tried with ggplot2 as:
library(ggplot2)
qplot(Data$X1, geom="density")
qplot(Data$X2, add = TRUE, geom="density")
Also in this case, plots appear separately (though I wrote add=TRUE)!! Can anyone come up with an easy solution to the problem, please?
In ggplot2 or lattice you need to reshape the data to seupose them.
For example :
dat <- data.frame(X1= rnorm(100),X2=rbeta(100,1,1))
library(reshape2)
dat.m <- melt(dat)
Using ``lattice`
densityplot(~value , groups = variable, data=dat.m,auto.key = T)
Using ``ggplot2`
ggplot(data=dat.m)+geom_density(aes(x=value, color=variable))
EDIT add X1+X2
Using lattice and the extended formua interface, it is extremely easy to do this:
densityplot(~X1+X2+I(X1+X2) , data=dat) ## no need to reshape data!!
You can try:
plot(density(Data$X1),col="red")
points(density(Data$X2),col="green")
I must add that the xlim and ylim values should ideally be set to include ranges of both X1 and X2, which could be done as follows:
foo <- density(Data$X1)
bar <- density(Data$X2)
plot(foo,col="red", xlim=c(min(foo$x,bar$x),max(foo$x,bar$x)) ylim=c(min(foo$y,bar$y),max(foo$y,bar$y))
points(bar,col="green")
In base graphics you can overlay density plots if you keep the ranges identical and use par(new=TRUE) between them. I think add=TRUE is a base graphics strategy that some functions but not all will honor.
If you specify n, from, and to in the calls to density and make sure that they match between the 2 calls then you should be able to use matplot to plot both in one step (you will need to bind the 2 sets of y values into a single matrix).
I'm trying to plot some data with 2d density contours using ggplot2 in R.
I'm getting one slightly odd result.
First I set up my ggplot object:
p <- ggplot(data, aes(x=Distance,y=Rate, colour = Company))
I then plot this with geom_points and geom_density2d. I want geom_density2d to be weighted based on the organisation's size (OrgSize variable). However when I add OrgSize as a weighting variable nothing changes in the plot:
This:
p+geom_point()+geom_density2d()
Gives an identical plot to this:
p+geom_point()+geom_density2d(aes(weight = OrgSize))
However, if I do the same with a loess line using geom_smooth, the weighting does make a clear difference.
This:
p+geom_point()+geom_smooth()
Gives a different plot to this:
p+geom_point()+geom_smooth(aes(weight=OrgSize))
I was wondering if I'm using density2d inappropriately, should I instead be using contour and supplying OrgSize as the 'height'? If so then why does geom_density2d accept a weighting factor?
Code below:
require(ggplot2)
Company <- c("One","One","One","One","One","Two","Two","Two","Two","Two")
Store <- c(1,2,3,4,5,6,7,8,9,10)
Distance <- c(1.5,1.6,1.8,5.8,4.2,4.3,6.5,4.9,7.4,7.2)
Rate <- c(0.1,0.3,0.2,0.4,0.4,0.5,0.6,0.7,0.8,0.9)
OrgSize <- c(500,1000,200,300,1500,800,50,1000,75,800)
data <- data.frame(Company,Store,Distance,Rate,OrgSize)
p <- ggplot(data, aes(x=Distance,y=Rate))
# Difference is apparent between these two
p+geom_point()+geom_smooth()
p+geom_point()+geom_smooth(aes(weight = OrgSize))
# Difference is not apparent between these two
p+geom_point()+geom_density2d()
p+geom_point()+geom_density2d(aes(weight = OrgSize))
geom_density2d is "accepting" the weight parameter, but then not passing to MASS::kde2d, since that function has no weights. As a consequence, you will need to use a different 2d-density method.
(I realize my answer is not addressing why the help page says that geom_density2d "understands" the weight argument, but when I have tried to calculate weighted 2D-KDEs, I have needed to use other packages besides MASS. Maybe this is a TODO that #hadley put in the help page that then got overlooked?)
Let's say I have the following dataset
bodysize=rnorm(20,30,2)
bodysize=sort(bodysize)
survive=c(0,0,0,0,0,1,0,1,0,0,1,1,0,1,1,1,0,1,1,1)
dat=as.data.frame(cbind(bodysize,survive))
I'm aware that the glm plot function has several nice plots to show you the fit,
but I'd nevertheless like to create an initial plot with:
1)raw data points
2)the loigistic curve and both
3)Predicted points
4)and aggregate points for a number of predictor levels
library(Hmisc)
plot(bodysize,survive,xlab="Body size",ylab="Probability of survival")
g=glm(survive~bodysize,family=binomial,dat)
curve(predict(g,data.frame(bodysize=x),type="resp"),add=TRUE)
points(bodysize,fitted(g),pch=20)
All fine up to here.
Now I want to plot the real data survival rates for a given levels of x1
dat$bd<-cut2(dat$bodysize,g=5,levels.mean=T)
AggBd<-aggregate(dat$survive,by=list(dat$bd),data=dat,FUN=mean)
plot(AggBd,add=TRUE)
#Doesn't work
I've tried to match AggBd to the dataset used for the model and all sort of other things but I simply can't plot the two together. Is there a way around this?
I basically want to overimpose the last plot along the same axes.
Besides this specific task I often wonder how to overimpose different plots that plot different variables but have similar scale/range on two-dimensional plots. I would really appreciate your help.
The first column of AggBd is a factor, you need to convert the levels to numeric before you can add the points to the plot.
AggBd$size <- as.numeric (levels (AggBd$Group.1))[AggBd$Group.1]
to add the points to the exisiting plot, use points
points (AggBd$size, AggBd$x, pch = 3)
You are best specifying your y-axis. Also maybe using par(new=TRUE)
plot(bodysize,survive,xlab="Body size",ylab="Probability of survival")
g=glm(survive~bodysize,family=binomial,dat)
curve(predict(g,data.frame(bodysize=x),type="resp"),add=TRUE)
points(bodysize,fitted(g),pch=20)
#then
par(new=TRUE)
#
plot(AggBd$Group.1,AggBd$x,pch=30)
obviously remove or change the axis ticks to prevent overlap e.g.
plot(AggBd$Group.1,AggBd$x,pch=30,xaxt="n",yaxt="n",xlab="",ylab="")
giving: